A comparative lattice analysis of SU(2)dark glueballs  

作　　者：Min-Huan Chu Jun-Hui Lai Wei Wang Jialu Zhang Qianteng Zhu 楚旻寰;赖俊辉;王伟;张家璐;朱潜腾

机构地区：[1]INPAC,Key Laboratory for Particle Astrophysics and Cosmology(MOE),Shanghai Key Laboratory for Particle Physics and Cosmology,School of Physics and Astronomy,Shanghai Jiao Tong University,Shanghai 200240,China [2]Yang Yuanqing Scientific Computering Center,Tsung-Dao Lee Institute,Shanghai Jiao Tong University,Shanghai 200240,China [3]Southern Center for Nuclear-Science Theory(SCNT),Institute of Modern Physics,Chinese Academy of Sciences,Huizhou 516000,China

出　　处：《Chinese Physics C》2024年第8期113-126,共14页中国物理C（英文版）

基　　金：Supported in part by the Natural Science Foundation of China(12125503,12335003);the Natural Science Foundation of Shandong Province,China(ZR2022ZD26)。

摘　　要：We study the mass and scattering cross section of SU(2)glueballs as dark matter candidates using lattice simulations.We employ both naive and improved SU(2)gauge actions in 3+1 dimensions with severalβvalues,and we adopt both the traditional Monte Carlo method and flow-based model based on machine learning techniques to generate lattice configurations.The mass of a dark scalar glueball with J^(PC)=0^(++)and the Nambu-Bethe-Salpeter wave function are calculated.Using a coupling constant ofβ=2.2 as an illustration,we compare the dark glueball mass calculated from the configurations generated from the two methods.While consistent results can be achieved,the two methods demonstrate distinct advantages.Using the Runge-Kutta method,we extract the glueball interaction potential and two-body scattering cross section.From the observational constraints,we obtain the lower bound of the mass of scalar glueballs as candidates of dark matter.

关 键 词：lattice QCD dark matter machine learning 

分 类 号：O572.243[理学—粒子物理与原子核物理]